Echinacoside promotes osteogenesis and angiogenesis and inhibits osteoclast formation.

PURPOSE: The purpose of this research is to demonstrate echinacoside promotes osteogenesis and angiogenesis and inhibits osteoclast formation. METHODS: We conducted a cell experiment in vitro to study how echinacoside affects angiogenesis, osteogenesis and osteoclast formation. We used polymerase chain reaction and Western blotting to detect the expression levels of proteins and genes related to angiogenesis, osteogenesis and osteoclast formation. We established a bone fracture model with rats to test angiogenesis, osteogenesis and osteoclast formation of echinacoside. We labelled osteogenic markers, blood vessels and osteoclastic markers in fracture sections of rats. RESULTS: The in vitro cell experiments showed echinacoside improved the osteogenic activity of mouse embryo osteoblast precursor cells and promoted the migration and tube formation of human umbilical vein endothelial cells. In addition, it inhibited differentiation of mouse leukaemia cells of monocyte macrophage. Echinacoside increased the expression of related proteins and genes and improved angiogenesis and osteogenesis while inhibiting osteoclast formation by repressing the expression of related proteins and genes. From in vivo experiments, the results of IHC and HE experiments demonstrated echinacoside significantly decreased the content of MMP-9 and improved the content of VEGF and OCN. The fluorescence immunoassay showed echinacoside promoted the activities of RUNX2 and VEGF and inhibited CTSK. Echinacoside reduced the content of TNF-α, IL-1ß and IL-6, thus demonstrating its anti-inflammatory activity. CONCLUSION: Echinacoside improved angiogenesis and osteogenesis and inhibited osteoclast formation to promote fracture healing.

Social participation, subjective well-being, and cognitive function as serial mediators between tooth loss and functional limitations in older Chinese adults.

BACKGROUND: Although tooth loss appears to be related to functional limitations, the mechanisms that underpin this relationship are unknown. We sought to address this knowledge gap by examining a multiple mediation hypothesis whereby tooth loss is predicted to indirectly affect functional limitations through social participation, subjective well-being, and cognitive function. METHODS: This study included 7,629 Chinese adults from the 2017/2018 Chinese Longitudinal Healthy Longevity Survey wave. The serial mediation effects were examined using Model 6 in the Hayes' PROCESS macro for SPSS. RESULTS: Tooth loss was significantly related to functional limitations. There was a direct (ß = - 0.0308; 95% CI, - 0.0131 to - 0.0036) and indirect (ß = - 0.0068; 95% CI, - 0.0096 to - 0.0041) association between tooth loss and instrumental activities of daily living (IADL) limitations, but only an indirect correlation with activities of daily living (ADL) limitations (ß = - 0.0188; 95% CI, - 0.0259 to - 0.0121). Social participation, subjective well-being, and cognitive function serially mediated the relationship between tooth loss and ADL/IADL limitations. CONCLUSION: The association between tooth loss and functional limitations is serially mediated by social participation, subjective well-being, and cognitive function. Our findings underscore the necessity of considering psychological and social factors as integrated healthcare approaches for the functional health of older adults.

HSH-UNet: Hybrid selective high order interactive U-shaped model for automated skin lesion segmentation.

The use of computer-assisted clinical dermatologists to diagnose skin diseases is an important aid. And computer-assisted techniques mainly use deep neural networks. Recently, the proposal of higher-order spatial interaction operations in deep neural networks has attracted a lot of attention. It has the advantages of both convolution and transformers, and additionally has the advantages of efficient, extensible and translation-equivariant. However, the selection of the interaction order in higher-order interaction operations requires tedious manual selection of a suitable interaction order. In this paper, a hybrid selective higher-order interaction U-shaped model HSH-UNet is proposed to solve the problem that requires manual selection of the order. Specifically, we design a hybrid selective high-order interaction module HSHB embedded in the U-shaped model. The HSHB adaptively selects the appropriate order for the interaction operation channel-by-channel under the computationally obtained guiding features. The hybrid order interaction also solves the problem of fixed order of interaction at each level. We performed extensive experiments on three public skin lesion datasets and our own dataset to validate the effectiveness of our proposed method. The ablation experiments demonstrate the effectiveness of our hybrid selective higher order interaction module. The comparison with state-of-the-art methods also demonstrates the superiority of our proposed HSH-UNet performance. The code is available at https://github.com/wurenkai/HSH-UNet.

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction. OBJECTIVE: The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC. METHODS: We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs. RESULTS: A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN. CONCLUSION: Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.

Luteolin inhibits A549 cells proliferation and migration by down-regulating androgen receptors.

BACKGROUND: Yi Fei Qing Hua Granules (YQG) is a traditional Chinese herbal medicine with the effects of inhibiting the proliferation of lung cancer cells. Luteolin is one of the active compounds of YQG. Luteolin is a common flavonoid extracted from natural herbs and it can promote cancer cells apoptosis has been reported. However, the underlying molecular mechanism and effects of luteolin on human lung cancer needs to be validated. METHODS: Molecular docking, network pharmacology methods and quantitative structure-activity relationship (QSAR) model were used to identify the active components of YQG and their possible mechanisms of action. Western blot analysis was used to measure AR expression in A549 cells. Cell migration assays were used to detect A549 cells proliferation transfected by AR plasmid and AR mutation plasmid, respectively. RESULTS: TCMSP search results revealed that there are 182 active compounds in YQG, which correspond to 232 target genes. Sixty-one genes were overlapping genes in the 2 datasets of TCMSP and GeneCards. Through bioinformatics tagging of these overlapping genes, a total of 1,951 GO functional tagging analysis and 133 KEGG pathways were obtained. Through molecular docking technology and QSAR model verification, the multi-target active compound luteolin was screened out as one of the active components of YQG for in vitro verification. Androgen receptor (AR) was the hub protein with the highest docking score of luteolin. Western blot showed that luteolin could inhibit AR protein expression in lung cancer cell line A549. After the phosphorylation site of AR protein 877 was inactivated, the ability of luteolin to inhibit the proliferation of lung cancer cells was weakened. Luteolin significantly inhibited the growth of A549 xenogeneic tumors at day 25 and 28 and inhibited the expression of AR. CONCLUSION: In this study, we have explored luteolin as one of the active components of YQG, and may inhibit the proliferation and migration of A549 cells by decreasing the expression of AR and the regulation of phosphorylation at AR-binding sites.

Automatic and efficient pneumothorax segmentation from CT images using EFA-Net with feature alignment function.

Pneumothorax is a condition involving a collapsed lung, which requires accurate segmentation of computed tomography (CT) images for effective clinical decision-making. Numerous convolutional neural network-based methods for medical image segmentation have been proposed, but they often struggle to balance model complexity with performance. To address this, we introduce the Efficient Feature Alignment Network (EFA-Net), a novel medical image segmentation network designed specifically for pneumothorax CT segmentation. EFA-Net uses EfficientNet as an encoder to extract features and a Feature Alignment (FA) module as a decoder to align features in both the spatial and channel dimensions. This design allows EFA-Net to achieve superior segmentation performance with reduced model complexity. In our dataset, our method outperforms various state-of-the-art methods in terms of accuracy and efficiency, achieving a Dice coefficient of 90.03%, an Intersection over Union (IOU) of 81.80%, and a sensitivity of 88.94%. Notably, EFA-Net has significantly lower FLOPs (1.549G) and parameters (0.432M), offering better robustness and facilitating easier deployment. Future work will explore the integration of downstream applications to enhance EFA-Net's utility for clinicians and patients in real-world diagnostic scenarios. The source code of EFA-Net is available at: https://github.com/tianjiamutangchun/EFA-Net .

Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications.

BACKGROUND: The cellular and molecular dynamics of human prepuce are crucial for understanding its biological and physiological functions, as well as the prevention of related genital diseases. However, the cellular compositions and heterogeneity of human prepuce at single-cell resolution are still largely unknown. Here we systematically dissected the prepuce of children and adults based on the single-cell RNA-seq data of 90,770 qualified cells. RESULTS: We identified 15 prepuce cell subtypes, including fibroblast, smooth muscle cells, T/natural killer cells, macrophages, vascular endothelial cells, and dendritic cells. The proportions of these cell types varied among different individuals as well as between children and adults. Moreover, we detected cell-type-specific gene regulatory networks (GRNs), which could contribute to the unique functions of related cell types. The GRNs were also highly dynamic between the prepuce cells of children and adults. Our cell-cell communication network analysis among different cell types revealed a set of child-specific (e.g., CD96, EPO, IFN-1, and WNT signaling pathways) and adult-specific (e.g., BMP10, NEGR, ncWNT, and NPR1 signaling pathways) signaling pathways. The variations of GRNs and cellular communications could be closely associated with prepuce development in children and prepuce maintenance in adults. CONCLUSIONS: Collectively, we systematically analyzed the cellular variations and molecular changes of the human prepuce at single-cell resolution. Our results gained insights into the heterogeneity of prepuce cells and shed light on the underlying molecular mechanisms of prepuce development and maintenance.

DAWTran: dynamic adaptive windowing transformer network for pneumothorax segmentation with implicit feature alignment.

Objective. This study aims to address the significant challenges posed by pneumothorax segmentation in computed tomography images due to the resemblance between pneumothorax regions and gas-containing structures such as the trachea and bronchus.Approach. We introduce a novel dynamic adaptive windowing transformer (DAWTran) network incorporating implicit feature alignment for precise pneumothorax segmentation. The DAWTran network consists of an encoder module, which employs a DAWTran, and a decoder module. We have proposed a unique dynamic adaptive windowing strategy that enables multi-head self-attention to effectively capture multi-scale information. The decoder module incorporates an implicit feature alignment function to minimize information deviation. Moreover, we utilize a hybrid loss function to address the imbalance between positive and negative samples.Main results. Our experimental results demonstrate that the DAWTran network significantly improves the segmentation performance. Specifically, it achieves a higher dice similarity coefficient (DSC) of 91.35% (a larger DSC value implies better performance), showing an increase of 2.21% compared to the TransUNet method. Meanwhile, it significantly reduces the Hausdorff distance (HD) to 8.06 mm (a smaller HD value implies better performance), reflecting a reduction of 29.92% in comparison to the TransUNet method. Incorporating the dynamic adaptive windowing (DAW) mechanism has proven to enhance DAWTran's performance, leading to a 4.53% increase in DSC and a 15.85% reduction in HD as compared to SwinUnet. The application of the implicit feature alignment (IFA) further improves the segmentation accuracy, increasing the DSC by an additional 0.11% and reducing the HD by another 10.01% compared to the model only employing DAW.Significance. These results highlight the potential of the DAWTran network for accurate pneumothorax segmentation in clinical applications, suggesting that it could be an invaluable tool in improving the precision and effectiveness of diagnosis and treatment in related healthcare scenarios. The improved segmentation performance with the inclusion of DAW and IFA validates the effectiveness of our proposed model and its components.

A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes.

Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes.

Prognostic risk assessment model and drug sensitivity analysis of colon adenocarcinoma (COAD) based on immune-related lncRNA pairs.

PURPOSE: The aim of this study was to identify and screen long non-coding RNA (lncRNA) associated with immune genes in colon cancer, construct immune-related lncRNA pairs, establish a prognostic risk assessment model for colon adenocarcinoma (COAD), and explore prognostic factors and drug sensitivity. METHOD: Our method was based on data from The Cancer Genome Atlas (TCGA). To begin, we obtained all pertinent demographic and clinical information on 385 patients with COAD. All lncRNAs significantly related to immune genes and with differential expression were identified to construct immune lncRNA pairs. Subsequently, least absolute shrinkage and selection operator and Cox models were used to screen out prognostic-related immune lncRNAs for the establishment of a prognostic risk scoring formula. Finally, We analysed the functional differences between subgroups and screened the drugs, and establish an individual prediction nomogram model. RESULTS: Our final analysis confirmed eight lncRNA pairs to construct prognostic risk assessment model. Results showed that the high-risk and low-risk groups had significant differences (training (n = 249): p < 0.001, validation (n = 114): p = 0.022). The prognostic model was certified as an independent prognosis model. Compared with the common clinicopathological indicators, the prognostic model had better predictive efficiency (area under the curve (AUC) = 0.805). Finally, We have analysed highly differentiated cellular pathways such as mucosal immune response, identified 9 differential immune cells, 10 sensitive drugs, and establish an individual prediction nomogram model (C-index = 0.820). CONCLUSION: Our study verified that the eight lncRNA pairs mentioned can be used as biomarkers to predict the prognosis of COAD patients. Identified cells, drugs may have an positive effect on colon cancer prognosis.

ADAN: An Adversarial Domain Adaptation Neural Network for Early Gastric Cancer Prediction.

Gastric cancer is a highly prevalent cancer world-wide. Accurate diagnosis of Early Gastric Cancer (EGC) is of great significance to improve the treatment and survival rate of patients. However, EGC and gastric ulcers have similar en-doscopic image characteristics, resulting in a high misdiagnosis rate. Most existing deep learning and machine learning models for EGC recognition have the disadvantages of cumbersome pre-processing steps and high leakage ratios. To address the above challenges, we propose an end-to-end Adversarial Do-main Adaptation Neural network (ADAN) for EGC prediction on endoscopic images. ADAN network consists of a source domain feature extractor, a source domain classifier, two target domain feature extractors, a target domain classifier, and a domain discriminator. A source domain feature extractor is designed to train the model on public gastrointestinal datasets, which effectively solves the problem of insufficient training data. In addition, an adaptive source-target domain mapping classifier is added to each target domain feature extractor for automatically adjusting the number of classification categories in the target domain. Experimental results show that the proposed ADAN network is superior to the most advanced methods and can accurately predict EGC in clinical practice. Clinical relevance-In this study, the EGC diagnosis model based on the adversarial domain adaptive construction will be more applicable to the real clinical scenario, with higher accuracy and sensitivity and assist the endoscopist to make more accurate diagnosis for EGC and reduce the workload.

Immunoprognostic model of lung adenocarcinoma and screening of sensitive drugs.

Screening of mRNAs and lncRNAs associated with prognosis and immunity of lung adenocarcinoma (LUAD) and used to construct a prognostic risk scoring model (PRS-model) for LUAD. To analyze the differences in tumor immune microenvironment between distinct risk groups of LUAD based on the model classification. The CMap database was also used to screen potential therapeutic compounds for LUAD based on the differential genes between distinct risk groups. he data from the Cancer Genome Atlas (TCGA) database. We divided the transcriptome data into a mRNA subset and a lncRNA subset, and use multiple methods to extract mRNAs and lncRNAs associated with immunity and prognosis. We further integrated the mRNA and lncRNA subsets and the corresponding clinical information, randomly divided them into training and test set according to the ratio of 5:5. Then, we performed the Cox risk proportional analysis and cross-validation on the training set to construct a LUAD risk scoring model. Based on the risk scoring model, patients were divided into distinct risk group. Moreover, we evaluate the prognostic performance of the model from the aspects of Area Under Curve (AUC) analysis, survival difference analysis, and independent prognostic analysis. We analyzed the differences in the expression of immune cells between the distinct risk groups, and also discuss the connection between immune cells and patient survival. Finally, we screened the potential therapeutic compounds of LUAD in the Connectivity Map (CMap) database based on differential gene expression profiles, and verified the compound activity by cytostatic assays. We extracted 26 mRNAs and 74 lncRNAs related to prognosis and immunity by using different screening methods. Two mRNAs (i.e., KLRC3 and RAET1E) and two lncRNAs (i.e., AL590226.1 and LINC00941) and their risk coefficients were finally used to construct the PRS-model. The risk score positions of the training and test set were 1.01056590 and 1.00925190, respectively. The expression of mRNAs involved in model construction differed significantly between the distinct risk population. The one-year ROC areas on the training and test sets were 0.735 and 0.681. There was a significant difference in the survival rate of the two groups of patients. The PRS-model had independent predictive capabilities in both training and test sets. Among them, in the group with low expression of M1 macrophages and resting NK cells, LUAD patients survived longer. In contrast, the monocyte expression up-regulated group survived longer. In the CMap drug screening, three LUAD therapeutic compounds, such as resveratrol, methotrexate, and phenoxybenzamine, scored the highest. In addition, these compounds had significant inhibitory effects on the LUAD A549 cell lines. The LUAD risk score model constructed using the expression of KLRC3, RAET1E, AL590226.1, LINC00941 and their risk coefficients had a good independent prognostic power. The optimal LUAD therapeutic compounds screened in the CMap database: resveratrol, methotrexate and phenoxybenzamine, all showed significant inhibitory effects on LUAD A549 cell lines.

Improvement of polydopamine-loaded salidroside on osseointegration of titanium implants.

BACKGROUND: Microarc oxidation (MAO) on the surface of medical pure titanium can improve its histocompatibility, and loading drugs on the surface can resist excessive intimal hyperplasia. METHODS: In this study, salidroside (SAL) was loaded on the surface of porous titanium (Ti) with polydopamine (PDA) carrier. The effects of SAL on the osteogenesis and angiogenesis of Ti implants were studied by phalloidin staining, alizarin red staining, ALP staining, wound-healing assay, cell transwell assay, matrigel tube formation, and osteogenic and angiogenic genes and proteins expression detected by PCR and western blot in vitro. The bone defect model experiments in rats was established in vivo including X-ray, micro CT, hematoxylin and eosin staining (HE), immunohistochemistry (IHC), Goldner's trichrome analysis, Safranin O-fast green staining and determination of contents of TNF-α and IL-6 in serum. RESULTS: EDS and EDS mapping showed that SAL could be loaded on the surface of the MAO coating by PDA. A drug release experiment showed that SAL loaded on the Ti coating could release slowly and stably without sudden release risk. In vitro cell experiments showed that the SAL coating could promote the proliferation, morphology, calcification and alkaline phosphate activity of MC3T3-E1 cells. At the same time, it promoted the migration and tube formation of HUVEC cells. The SAL coating promoted osteogenesis and angiogenesis by promoting the expression of genes and proteins related to. In vivo experiments, HE and IHC showed that SAL significantly promoted the expression of COL-1 and CD31. Goldner's trichrome and Safranin O-fast green staining showed that SAL coating could increase the new bone tissue around the implantation site. The SAL coating had anti-inflammatory activity by reducing the levels of TNF-α and IL-6 in vivo. CONCLUSION: Therefore, SAL could improve osteogenesis and angiogenesis in conjunction with the Ti-PDA coating.

Copper coating formed by micro-arc oxidation on pure Mg improved antibacterial activity, osteogenesis, and angiogenesis in vivo and in vitro.

Micro-arc oxidation (MAO) was used to improve the resistance of pure magnesium (Mg). Copper (Cu), a good antibacterial, angiogenic, and osteogenic element, was added by reaction in a Cu-containing electrolyte to improve the osteogenic and pro-angiogenic activities of Mg. The surface microstructures of the resulting MAO were evaluated by a scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDS) mapping. The release of Cu ions was detected by ICP-OES. The antibacterial activity of films with different concentrations of Cu ions was assessed against Staphylococcus aureus (S. aureus). The osteogenesis of films was confirmed by cell morphology and proliferation, ALP activity, alizarin red staining, and osteogenic-related gene expression in the MC3T3-E1 cell line. The angiogenesis of the films was tested in human umbilical vein endothelial cells (HUVECs) by cell migration, tube formation, and VEGF quantification in vitro, and by a chicken embryo chorioallantoic membrane (CAM) assay in vivo. The results showed that the microporous structure was shaped by MAO, and the Cu group was denser and more uniform. The Cu coating showed effective antibacterial activity against S. aureus while also enhancing osteogenesis and angiogenesis in vitro. According to the CAM assay, the Cu group showed not only biocompatibility but also a significant angiogenic response, which was consistent with in vitro studies. The findings indicate that a Cu coating on Mg-MAO enhances osteogenesis and angiogenesis.